Overview

Dataset statistics

Number of variables13
Number of observations2969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.7 KiB
Average record size in memory104.0 B

Variable types

Numeric13

Alerts

gross_revenue is highly correlated with qtde_invoices and 3 other fieldsHigh correlation
recency_days is highly correlated with qtde_invoicesHigh correlation
qtde_invoices is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with avg_unique_basket_sizeHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with qtde_products and 1 other fieldsHigh correlation
gross_revenue is highly correlated with qtde_invoices and 1 other fieldsHigh correlation
qtde_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 1 other fieldsHigh correlation
qtde_products is highly correlated with qtde_invoicesHigh correlation
avg_ticket is highly correlated with qtde_returns and 1 other fieldsHigh correlation
qtde_returns is highly correlated with avg_ticket and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with avg_ticket and 1 other fieldsHigh correlation
gross_revenue is highly correlated with qtde_invoices and 2 other fieldsHigh correlation
qtde_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with qtde_itemsHigh correlation
gross_revenue is highly correlated with qtde_invoices and 5 other fieldsHigh correlation
qtde_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 5 other fieldsHigh correlation
qtde_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_ticket is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_returns is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 53.4442279) Skewed
frequency is highly skewed (γ1 = 49.02784848) Skewed
qtde_returns is highly skewed (γ1 = 51.79774426) Skewed
avg_basket_size is highly skewed (γ1 = 44.68328098) Skewed
df_index has unique values Unique
customer_id has unique values Unique
recency_days has 34 (1.1%) zeros Zeros
frequency has 195 (6.6%) zeros Zeros
qtde_returns has 1481 (49.9%) zeros Zeros

Reproduction

Analysis started2022-08-19 19:28:42.484357
Analysis finished2022-08-19 19:29:04.192251
Duration21.71 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2317.292354
Minimum0
Maximum5715
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-08-19T16:29:04.272377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.4
Q1929
median2120
Q33537
95-th percentile5035.2
Maximum5715
Range5715
Interquartile range (IQR)2608

Descriptive statistics

Standard deviation1554.944589
Coefficient of variation (CV)0.6710178739
Kurtosis-1.010787014
Mean2317.292354
Median Absolute Deviation (MAD)1271
Skewness0.342284058
Sum6880041
Variance2417852.674
MonotonicityStrictly increasing
2022-08-19T16:29:04.370064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
30111
 
< 0.1%
29961
 
< 0.1%
29991
 
< 0.1%
30001
 
< 0.1%
30011
 
< 0.1%
30021
 
< 0.1%
30051
 
< 0.1%
30071
 
< 0.1%
30081
 
< 0.1%
Other values (2959)2959
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
57151
< 0.1%
56961
< 0.1%
56861
< 0.1%
56801
< 0.1%
56591
< 0.1%
56551
< 0.1%
56491
< 0.1%
56381
< 0.1%
56371
< 0.1%
56271
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.77299
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-08-19T16:29:04.479300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.4
Q113799
median15221
Q316768
95-th percentile17964.6
Maximum18287
Range5940
Interquartile range (IQR)2969

Descriptive statistics

Standard deviation1718.990292
Coefficient of variation (CV)0.1125673398
Kurtosis-1.206094692
Mean15270.77299
Median Absolute Deviation (MAD)1488
Skewness0.03160785866
Sum45338925
Variance2954927.624
MonotonicityNot monotonic
2022-08-19T16:29:04.578655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
175881
 
< 0.1%
149051
 
< 0.1%
161031
 
< 0.1%
146261
 
< 0.1%
148681
 
< 0.1%
182461
 
< 0.1%
171151
 
< 0.1%
166111
 
< 0.1%
159121
 
< 0.1%
Other values (2959)2959
99.7%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123641
< 0.1%
123701
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182771
< 0.1%
182761
< 0.1%
182741
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182691
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2954
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.226056
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-08-19T16:29:04.681817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.77
Q1570.96
median1086.92
Q32308.06
95-th percentile7219.68
Maximum279138.02
Range279131.82
Interquartile range (IQR)1737.1

Descriptive statistics

Standard deviation10580.4905
Coefficient of variation (CV)3.848534202
Kurtosis353.9585684
Mean2749.226056
Median Absolute Deviation (MAD)672.72
Skewness16.77787915
Sum8162452.16
Variance111946779.3
MonotonicityNot monotonic
2022-08-19T16:29:04.776238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178.962
 
0.1%
533.332
 
0.1%
889.932
 
0.1%
2053.022
 
0.1%
745.062
 
0.1%
379.652
 
0.1%
2092.322
 
0.1%
731.92
 
0.1%
1353.742
 
0.1%
3312
 
0.1%
Other values (2944)2949
99.3%
ValueCountFrequency (%)
6.21
< 0.1%
13.31
< 0.1%
151
< 0.1%
36.561
< 0.1%
451
< 0.1%
521
< 0.1%
52.21
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
140438.721
< 0.1%
124564.531
< 0.1%
117375.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.28864938
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-08-19T16:29:04.880099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.75617089
Coefficient of variation (CV)1.209485215
Kurtosis2.778038567
Mean64.28864938
Median Absolute Deviation (MAD)26
Skewness1.798396863
Sum190873
Variance6046.022112
MonotonicityNot monotonic
2022-08-19T16:29:04.979633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.3%
487
 
2.9%
285
 
2.9%
385
 
2.9%
876
 
2.6%
1067
 
2.3%
966
 
2.2%
766
 
2.2%
1764
 
2.2%
2255
 
1.9%
Other values (262)2219
74.7%
ValueCountFrequency (%)
034
 
1.1%
199
3.3%
285
2.9%
385
2.9%
487
2.9%
543
1.4%
766
2.2%
876
2.6%
966
2.2%
1067
2.3%
ValueCountFrequency (%)
3732
0.1%
3724
0.1%
3711
 
< 0.1%
3681
 
< 0.1%
3664
0.1%
3652
0.1%
3641
 
< 0.1%
3601
 
< 0.1%
3591
 
< 0.1%
3584
0.1%

qtde_invoices
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.72280229
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-08-19T16:29:05.085478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.85665393
Coefficient of variation (CV)1.547607882
Kurtosis190.8253633
Mean5.72280229
Median Absolute Deviation (MAD)2
Skewness10.76645634
Sum16991
Variance78.44031883
MonotonicityNot monotonic
2022-08-19T16:29:05.185322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2786
26.5%
3498
16.8%
4393
13.2%
5237
 
8.0%
1190
 
6.4%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
Other values (46)332
11.2%
ValueCountFrequency (%)
1190
 
6.4%
2786
26.5%
3498
16.8%
4393
13.2%
5237
 
8.0%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%
571
< 0.1%

qtde_items
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1665
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1606.461098
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-08-19T16:29:05.289886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile101.4
Q1296
median639
Q31399
95-th percentile4407.4
Maximum196844
Range196843
Interquartile range (IQR)1103

Descriptive statistics

Standard deviation5882.976527
Coefficient of variation (CV)3.6620722
Kurtosis467.153716
Mean1606.461098
Median Absolute Deviation (MAD)420
Skewness17.87844459
Sum4769583
Variance34609412.81
MonotonicityNot monotonic
2022-08-19T16:29:05.392353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
889
 
0.3%
1509
 
0.3%
2608
 
0.3%
848
 
0.3%
2888
 
0.3%
2728
 
0.3%
2468
 
0.3%
5167
 
0.2%
3947
 
0.2%
Other values (1655)2886
97.2%
ValueCountFrequency (%)
11
< 0.1%
22
0.1%
122
0.1%
161
< 0.1%
171
< 0.1%
181
< 0.1%
191
< 0.1%
201
< 0.1%
231
< 0.1%
251
< 0.1%
ValueCountFrequency (%)
1968441
< 0.1%
809971
< 0.1%
799631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
628121
< 0.1%
582431
< 0.1%
577851
< 0.1%

qtde_products
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct469
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.705288
Minimum1
Maximum7837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-08-19T16:29:05.506452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7837
Range7836
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.8419967
Coefficient of variation (CV)2.199106503
Kurtosis354.8373546
Mean122.705288
Median Absolute Deviation (MAD)44
Skewness15.70613971
Sum364312
Variance72814.70321
MonotonicityNot monotonic
2022-08-19T16:29:05.607946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2845
 
1.5%
2038
 
1.3%
3535
 
1.2%
1533
 
1.1%
2933
 
1.1%
1933
 
1.1%
1132
 
1.1%
2631
 
1.0%
2730
 
1.0%
2529
 
1.0%
Other values (459)2630
88.6%
ValueCountFrequency (%)
16
 
0.2%
214
0.5%
316
0.5%
417
0.6%
526
0.9%
629
1.0%
718
0.6%
819
0.6%
927
0.9%
1027
0.9%
ValueCountFrequency (%)
78371
< 0.1%
56701
< 0.1%
50951
< 0.1%
45771
< 0.1%
26981
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16731
< 0.1%
16361
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2966
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.90005685
Minimum2.150588235
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-08-19T16:29:05.714447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.916661099
Q113.11933333
median17.97438356
Q324.98828571
95-th percentile90.497
Maximum56157.5
Range56155.34941
Interquartile range (IQR)11.86895238

Descriptive statistics

Standard deviation1036.934336
Coefficient of variation (CV)19.9794451
Kurtosis2890.70744
Mean51.90005685
Median Absolute Deviation (MAD)5.994222271
Skewness53.4442279
Sum154091.2688
Variance1075232.818
MonotonicityNot monotonic
2022-08-19T16:29:05.809653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152
 
0.1%
4.1622
 
0.1%
14.478333332
 
0.1%
18.152222221
 
< 0.1%
13.927368421
 
< 0.1%
36.244117651
 
< 0.1%
29.784166671
 
< 0.1%
22.87926231
 
< 0.1%
20.511041671
 
< 0.1%
149.0251
 
< 0.1%
Other values (2956)2956
99.6%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
2.651
< 0.1%
2.6569318181
< 0.1%
2.7075982531
< 0.1%
2.7606215721
< 0.1%
2.7704641911
< 0.1%
ValueCountFrequency (%)
56157.51
< 0.1%
4453.431
< 0.1%
3202.921
< 0.1%
1687.21
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%
841.02144931
< 0.1%
651.16833331
< 0.1%
6401
< 0.1%
624.41
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.35143043
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-08-19T16:29:05.911156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.92857143
median48.28571429
Q385.33333333
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)59.4047619

Descriptive statistics

Standard deviation63.54282948
Coefficient of variation (CV)0.9434518178
Kurtosis4.887703174
Mean67.35143043
Median Absolute Deviation (MAD)26.28571429
Skewness2.062908983
Sum199966.397
Variance4037.691178
MonotonicityNot monotonic
2022-08-19T16:29:06.012231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1425
 
0.8%
422
 
0.7%
7021
 
0.7%
720
 
0.7%
3519
 
0.6%
4918
 
0.6%
2117
 
0.6%
4617
 
0.6%
1117
 
0.6%
116
 
0.5%
Other values (1248)2777
93.5%
ValueCountFrequency (%)
116
0.5%
1.51
 
< 0.1%
213
0.4%
2.51
 
< 0.1%
2.6013986011
 
< 0.1%
315
0.5%
3.3214285711
 
< 0.1%
3.3303571431
 
< 0.1%
3.52
 
0.1%
422
0.7%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3631
 
< 0.1%
3621
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1229
Distinct (%)41.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05775611583
Minimum0
Maximum34
Zeros195
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-08-19T16:29:06.117833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01418439716
median0.02307692308
Q30.0401459854
95-th percentile0.1176470588
Maximum34
Range34
Interquartile range (IQR)0.02596158824

Descriptive statistics

Standard deviation0.6472555286
Coefficient of variation (CV)11.20670113
Kurtosis2553.18188
Mean0.05775611583
Median Absolute Deviation (MAD)0.01095571096
Skewness49.02784848
Sum171.4779079
Variance0.4189397193
MonotonicityNot monotonic
2022-08-19T16:29:06.218020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0195
 
6.6%
0.0714285714316
 
0.5%
0.0476190476215
 
0.5%
0.0158730158714
 
0.5%
0.030303030314
 
0.5%
0.0285714285714
 
0.5%
0.142857142913
 
0.4%
0.0238095238113
 
0.4%
0.0645161290313
 
0.4%
0.117647058812
 
0.4%
Other values (1219)2650
89.3%
ValueCountFrequency (%)
0195
6.6%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055096418731
 
< 0.1%
0.0056022408962
 
0.1%
0.0056179775281
 
< 0.1%
0.0056338028172
 
0.1%
0.0056818181821
 
< 0.1%
0.0056980056982
 
0.1%
ValueCountFrequency (%)
341
 
< 0.1%
61
 
< 0.1%
41
 
< 0.1%
26
0.2%
1.51
 
< 0.1%
1.3333333332
 
0.1%
14
0.1%
0.66666666673
0.1%
0.55227882041
 
< 0.1%
0.53494623661
 
< 0.1%

qtde_returns
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct214
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.1569552
Minimum0
Maximum80995
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-08-19T16:29:06.324798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100.6
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1512.496135
Coefficient of variation (CV)24.33349783
Kurtosis2765.52864
Mean62.1569552
Median Absolute Deviation (MAD)1
Skewness51.79774426
Sum184544
Variance2287644.557
MonotonicityNot monotonic
2022-08-19T16:29:06.431601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2148
 
5.0%
3105
 
3.5%
489
 
3.0%
678
 
2.6%
561
 
2.1%
1251
 
1.7%
843
 
1.4%
743
 
1.4%
Other values (204)706
23.8%
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2148
 
5.0%
3105
 
3.5%
489
 
3.0%
561
 
2.1%
678
 
2.6%
743
 
1.4%
843
 
1.4%
941
 
1.4%
ValueCountFrequency (%)
809951
< 0.1%
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1973
Distinct (%)66.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.349541
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-08-19T16:29:06.546854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.25
median172
Q3281.5
95-th percentile599.52
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.25

Descriptive statistics

Standard deviation791.5024106
Coefficient of variation (CV)3.174268569
Kurtosis2256.245507
Mean249.349541
Median Absolute Deviation (MAD)82.75
Skewness44.68328098
Sum740318.7873
Variance626476.066
MonotonicityNot monotonic
2022-08-19T16:29:06.652040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
11410
 
0.3%
739
 
0.3%
869
 
0.3%
829
 
0.3%
1368
 
0.3%
608
 
0.3%
758
 
0.3%
888
 
0.3%
717
 
0.2%
Other values (1963)2882
97.1%
ValueCountFrequency (%)
12
0.1%
21
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
40498.51
< 0.1%
6009.3333331
< 0.1%
42821
< 0.1%
39061
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
28011
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%

avg_unique_basket_size
Real number (ℝ≥0)

HIGH CORRELATION

Distinct910
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.48500567
Minimum0.2
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-08-19T16:29:06.761436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.666666667
median13.6
Q322
95-th percentile46
Maximum259
Range258.8
Interquartile range (IQR)14.33333333

Descriptive statistics

Standard deviation15.46225751
Coefficient of variation (CV)0.8843152701
Kurtosis29.29580888
Mean17.48500567
Median Absolute Deviation (MAD)6.6
Skewness3.433836262
Sum51912.98183
Variance239.0814073
MonotonicityNot monotonic
2022-08-19T16:29:07.172515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1343
 
1.4%
942
 
1.4%
1641
 
1.4%
839
 
1.3%
1737
 
1.2%
1437
 
1.2%
736
 
1.2%
1136
 
1.2%
534
 
1.1%
1534
 
1.1%
Other values (900)2590
87.2%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.253
 
0.1%
0.33333333336
0.2%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
0.512
0.4%
0.54545454551
 
< 0.1%
0.55555555561
 
< 0.1%
0.57142857141
 
< 0.1%
0.61764705881
 
< 0.1%
ValueCountFrequency (%)
2591
< 0.1%
1771
< 0.1%
1481
< 0.1%
1271
< 0.1%
1051
< 0.1%
1041
< 0.1%
1011
< 0.1%
981
< 0.1%
95.51
< 0.1%
94.333333331
< 0.1%

Interactions

2022-08-19T16:29:02.583453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:46.560007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:47.928892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:49.181888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:50.558235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:51.815491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:52.986100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:54.477235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:55.747221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:56.956343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:58.427150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:59.695306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:01.017747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:02.690942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:46.673025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:48.021845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:49.277330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:50.649950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:51.905498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:53.080200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:54.580608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:55.837758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:57.050670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:58.525552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:59.793269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:01.114035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:02.793964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:46.764474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:48.111386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:49.370024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:50.741507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:51.991921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:53.332011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:54.674866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:55.925951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:57.337842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:58.619105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:59.892917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:01.212394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:02.896995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:46.857168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:48.205378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:49.464955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:50.833527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:52.077921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:53.435474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:54.770721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:56.019180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:57.431378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:58.713059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:59.997564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:01.306804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:02.992020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:46.949305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:48.300400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:49.563502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:50.928555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:52.170942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:53.545478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:54.870955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:56.111526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:57.530421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:58.811080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:00.119603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:01.407883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:03.085684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:47.038759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:48.387430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:49.658107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:51.021114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:52.252620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:53.642193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:54.963188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:56.196549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:57.619792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:58.902603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:00.207100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:01.503250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:03.186405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:47.137377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:48.494139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:49.760166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:51.123517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:52.346116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:53.750854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:55.065955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:56.296720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:57.722130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:59.003114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:00.311073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:01.608229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:03.285902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:47.355733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:48.595285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:49.863928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:51.219635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:52.442135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:53.861032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:55.165398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:56.396193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:57.827158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:59.109147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:00.417640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:01.710264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:03.377143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:47.445897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:48.689229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:50.084916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:51.312128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:52.527511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:53.955067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:55.257417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:56.485399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:57.923676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:59.201840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:00.513697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:01.802835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:03.470711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:47.540028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:48.787127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:50.181025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:51.407003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:52.619539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:54.056184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:55.354460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:56.581504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:58.023931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:59.303851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:00.613967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:01.906386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:03.563757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:47.633137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:48.887387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:50.274542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:51.502627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:52.707562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:54.164042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:55.452699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:56.674037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:58.118140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:59.400349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:00.714341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:02.003909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:03.658826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:47.733831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:48.988189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:50.371430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:51.600882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:52.802617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:54.277069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:55.552420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:56.771311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:58.223163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:59.498580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:00.818471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:02.358986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:03.757848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:47.835334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:49.088454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:50.467711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:51.722084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:52.901085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:54.382091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:55.656199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:56.868326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:58.333186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:28:59.599611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:00.924267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-19T16:29:02.475265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-19T16:29:07.267252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-19T16:29:07.419046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-19T16:29:07.564202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-19T16:29:07.709756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-19T16:29:03.902126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-19T16:29:04.098261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
00178505391.21372.034.01733.0297.018.15222235.50000034.00000040.050.9705880.617647
11130473232.5956.09.01390.0171.018.90403527.2500000.02839135.0154.44444411.666667
22125836705.382.015.05028.0232.028.90250023.1875000.04043150.0335.2000007.600000
3313748948.2595.05.0439.028.033.86607192.6666670.0179860.087.8000004.800000
4415100876.00333.03.080.03.0292.0000008.6000000.07500022.026.6666670.333333
55152914623.3025.014.02102.0102.045.32647123.2000000.04023029.0150.1428574.357143
66146885630.877.021.03621.0327.017.21978618.3000000.057377399.0172.4285717.047619
77178095411.9116.012.02057.061.088.71983635.7000000.03361341.0171.4166673.833333
881531160767.900.091.038194.02379.025.5434644.1444440.243968474.0419.7142866.230769
99160982005.6387.07.0613.067.029.93477647.6666670.0244760.087.5714294.857143

Last rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
29595627177271060.2515.01.0645.066.016.0643946.00.0000006.0645.00000066.000000
2960563717232421.522.02.0203.036.011.70888912.00.1666670.0101.50000015.000000
2961563817468137.0010.02.0116.05.027.4000004.00.5000000.058.0000002.500000
2962564913596697.045.02.0406.0166.04.1990367.00.2857140.0203.00000066.500000
29635655148931237.859.02.0799.073.016.9568492.01.0000000.0399.50000036.000000
2964565912479473.2011.01.0382.030.015.7733334.00.00000034.0382.00000030.000000
2965568014126706.137.03.0508.015.047.0753333.01.00000050.0169.3333334.666667
29665686135211092.391.03.0733.0435.02.5112414.50.3333330.0244.333333104.000000
2967569615060301.848.04.0262.0120.02.5153331.04.0000000.065.50000020.000000
2968571512558269.967.01.0196.011.024.5418186.00.000000196.0196.00000011.000000